Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data

• URL: http://arxiv.org/abs/2406.11046v1
• Date: Sun, 16 Jun 2024 19:11:15 GMT
• Title: Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data
• Authors: Alexander Quispe, Rodrigo Grijalba,
• Abstract summary: ChatGPT is an AI tool that enhances software production efficiency. We estimate ChatGPT's effects on the number of git pushes, repositories, and unique developers per 100,000 people. These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.
• Score: 49.1574468325115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancements in Artificial Intelligence, particularly with ChatGPT, have significantly impacted software development. Utilizing novel data from GitHub Innovation Graph, we hypothesize that ChatGPT enhances software production efficiency. Utilizing natural experiments where some governments banned ChatGPT, we employ Difference-in-Differences (DID), Synthetic Control (SC), and Synthetic Difference-in-Differences (SDID) methods to estimate its effects. Our findings indicate a significant positive impact on the number of git pushes, repositories, and unique developers per 100,000 people, particularly for high-level, general purpose, and shell scripting languages. These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.

Related papers

• Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects [0.0]
  GitHub Copilot is an AI-powered coding assistant. This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot.
  arXiv  Detail & Related papers  (2024-06-25T19:51:21Z)
• ChatGPT as a Software Development Bot: A Project-based Study [5.518217604591736]
  This study examines the impact of generative AI tools, specifically ChatGPT, on the software development experiences of undergraduate students. Results showed that ChatGPT significantly addresses skill gaps in software development education, enhancing efficiency, accuracy, and collaboration.
  arXiv  Detail & Related papers  (2023-10-20T16:48:19Z)
• ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? [24.61869093475626]
  Large language models (LLMs) like ChatGPT exhibited remarkable advancement in a range of software engineering tasks. We compare ChatGPT with state-of-the-art language models designed for software vulnerability purposes. We found that ChatGPT achieves limited performance, trailing behind other language models in vulnerability contexts by a significant margin.
  arXiv  Detail & Related papers  (2023-10-15T12:01:35Z)
• Using ChatGPT as a Static Application Security Testing Tool [0.0]
  ChatGPT has caught a huge amount of attention with its remarkable performance. We study the feasibility of using ChatGPT for vulnerability detection in Python source code.
  arXiv  Detail & Related papers  (2023-08-28T09:21:37Z)
• SoTaNa: The Open-Source Software Development Assistant [81.86136560157266]
  SoTaNa is an open-source software development assistant. It generates high-quality instruction-based data for the domain of software engineering. It employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA.
  arXiv  Detail & Related papers  (2023-08-25T14:56:21Z)
• Comparing Software Developers with ChatGPT: An Empirical Investigation [0.0]
  This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration.
  arXiv  Detail & Related papers  (2023-05-19T17:25:54Z)
• Does Synthetic Data Generation of LLMs Help Clinical Text Mining? [51.205078179427645]
  We investigate the potential of OpenAI's ChatGPT to aid in clinical text mining. We propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data. Our method has resulted in significant improvements in the performance of downstream tasks.
  arXiv  Detail & Related papers  (2023-03-08T03:56:31Z)
• AugGPT: Leveraging ChatGPT for Text Data Augmentation [59.76140039943385]
  We propose a text data augmentation approach based on ChatGPT (named AugGPT) AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach.
  arXiv  Detail & Related papers  (2023-02-25T06:58:16Z)
• A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity [79.12003701981092]
  We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning.
  arXiv  Detail & Related papers  (2023-02-08T12:35:34Z)
• Bayesian Imitation Learning for End-to-End Mobile Manipulation [80.47771322489422]
  Augmenting policies with additional sensor inputs, such as RGB + depth cameras, is a straightforward approach to improving robot perception capabilities. We show that using the Variational Information Bottleneck to regularize convolutional neural networks improves generalization to held-out domains. We demonstrate that our method is able to help close the sim-to-real gap and successfully fuse RGB and depth modalities.
  arXiv  Detail & Related papers  (2022-02-15T17:38:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.